Systematic evaluation of label-free protein quantification pipelines in 12 mouse syngeneic models

Background: The widespread application of omics technologies in the past two decades has significantly expanded our knowledge cancer biology. Proteins act as link between genetic code and phenotype can better reflect dynamic state a cell. With rapid advances field proteomics, especially label-free proteomics quantification technology powered by LC-MS/MS, it is now routine to characterize quantify thousands proteins tumor samples timely manner. Data-dependent acquisition (DDA) been workhorse for bottom-up within few years, however, suffers from high percentage missing data originating its stochastic nature. this, data-independent (DIA) gained increasing popularity due higher reproducibility improved completeness. In this study, was used profile proteome 12 widely mouse syngeneic models, systematic evaluation performance different protein pipelines conducted. Methods: A total 120 were prepared models batches. Five collected per model when volume reached ∼500 mm3 (batch one) ∼1000 two). All analyzed DDA using MaxQuant software, three each batch DIA Spectronaut 15.0 software. Seven abundance matrices generated pipelines, based on combinations parameters, algorithms, methods. Missing analyses, sample similarity mRNA: correlation analyses conducted evaluate quality data. Results: outperformed many aspects, including coverage, reproducibility, discrimination power among correlation. More specifically, number with no all far outnumbered that DDA, much lower than DDA. On average had smaller coefficient variation measurement biological replicate unit. Further, greater differentiation observed Finally, slightly Conclusion: protocol developed data, found offers more comprehensive picture panel reproducibility. No conflict interest.